scholarly journals Estimating Impervious Surface Distribution: A Comparison of Object Based Analysis and Spectral Mixture Analysis

2015 ◽  
Author(s):  
Keyword(s):  
Impervious Surface ◽  
Spectral Mixture Analysis ◽  
Mixture Analysis ◽  
Surface Distribution ◽  
Object Based ◽  
Spectral Mixture

scholarly journals Extraction of High-Precision Urban Impervious Surfaces from Sentinel-2 Multispectral Imagery via Modified Linear Spectral Mixture Analysis

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2873 ◽  
Author(s):  
Rudong Xu ◽  
Jin Liu ◽  
Jianhui Xu
Keyword(s):  
High Precision ◽  
Impervious Surface ◽  
Spectral Mixture Analysis ◽  
Landsat 8 ◽  
Impervious Surfaces ◽  
Mixture Analysis ◽  
Spectral Bands ◽  
Linear Spectral Mixture Analysis ◽  
Sentinel 2A ◽  
Spectral Mixture

This study explores the performance of Sentinel-2A Multispectral Instrument (MSI) imagery for extracting urban impervious surface using a modified linear spectral mixture analysis (MLSMA) method. Sentinel-2A MSI provided 10 m red, green, blue, and near-infrared spectral bands, and 20 m shortwave infrared spectral bands, which were used to extract impervious surfaces. We aimed to extract urban impervious surfaces at a spatial resolution of 10 m in the main urban area of Guangzhou, China. In MLSMA, a built-up image was first extracted from the normalized difference built-up index (NDBI) using the Otsu’s method; the high-albedo, low-albedo, vegetation, and soil fractions were then estimated using conventional linear spectral mixture analysis (LSMA). The LSMA results were post-processed to extract high-precision impervious surface, vegetation, and soil fractions by integrating the built-up image and the normalized difference vegetation index (NDVI). The performance of MLSMA was evaluated using Landsat 8 Operational Land Imager (OLI) imagery. Experimental results revealed that MLSMA can extract the high-precision impervious surface fraction at 10 m with Sentinel-2A imagery. The 10 m impervious surface map of Sentinel-2A is capable of recovering more detail than the 30 m map of Landsat 8. In the Sentinel-2A impervious surface map, continuous roads and the boundaries of buildings in urban environments were clearly identified.

Assessing and monitoring semi-arid shrublands using object-based image analysis and multiple endmember spectral mixture analysis

2012 ◽  
Vol 185 (4) ◽  
pp. 3173-3190 ◽  
Author(s):  
Yuki Hamada ◽  
Douglas A. Stow ◽  
Dar A. Roberts ◽  
Janet Franklin ◽  
Phaedon C. Kyriakidis
Keyword(s):  
Image Analysis ◽  
Spectral Mixture Analysis ◽  
Mixture Analysis ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Arid Shrublands ◽  
Spectral Mixture ◽  
Semi Arid
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